Abstract

Purpose – This paper aims to investigate the efficiency and effectiveness of alternative credit-scoring models for consumer loans in the banking sector. In particular, the focus is upon the financial risks associated with both the efficiency of alternative models in terms of correct classification rates, and their effectiveness in terms of misclassification costs (MCs).

Design/methodology/approach – A data set of 630 loan applicants was provided by an Egyptian private bank. A two-thirds training sample was selected for building the proposed models, leaving a one-third testing sample to evaluate the predictive ability of the models. In this paper, an investigation is conducted into both neural nets (NNs), such as probabilistic and multi-layer feed-forward neural nets, and conventional techniques, such as the weight of evidence measure, discriminant analysis and logistic regression.

Findings – The results revealed that a best net search, which selected a multi-layer feed-forward net with five nodes, generated both the most efficient classification rate and the most effective MC. In general, NNs gave better average correct classification rates and lower MCs than traditional techniques.

Practical implications – By reducing the financial risks associated with loan defaults, banks can achieve a more effective management of such a crucial component of their operations, namely, the provision of consumer loans.

Originality/value – The use of NNs and conventional techniques in evaluating consumer loans within the Egyptian private banking sector utilizes rigorous techniques in an environment which merits investigation.